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Achieve interoperability in clinical development
Harness heterogeneous data sources for advanced analytics
Mar 17, 2021
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  • Achieve interoperability in clinical development

Life science companies are facing an explosion of structured and unstructured data sources. Patient-level data from connected mobile devices and patient-centric applications are increasing in complexity and quantity.

Clinical teams know that tremendous value can be unlocked from this data – to track trial performance against objectives, scrutinize sites for risk, identify new indications for compounds by studying real world evidence, and numerous other use cases. Yet most organizations struggle to ingest and process this data for investigation, and there are several reasons why.

R&D organizations have made major investments in information technology but find it increasingly difficult to derive value from the accelerating amount of available data. This patchwork of risk-based monitoring, site management, and clinical research associate systems performs a narrow set of tasks and has a limited ability to exchange data. Even today stakeholders exchange data manually by switching between standalone apps (e.g., from EDC to a CTMS) or copying MS Excel files. These approaches are both time-consuming and prone to errors.

Life science companies require a solution to ingest and manage data holistically and empower users to perform machine-learning enabled analytics. Such a solution should be agile and adapt to decentralized trials and other new models. The high volume and velocity of data demonstrate the need for machine learning to automate certain data processing and augment analytics. Equally important is the ability to connect heterogeneous data and systems that have historically been difficult to link.

Customers evaluating enterprise-grade clinical data and analytics solutions should include these capabilities in their criteria:

  • Collect and clean data from heterogeneous sources including clinical and business applications, mobile apps, eTMF files, and more.
  • Provide a central repository that houses structured and unstructured data sets at scale.
  • Enable stakeholders across the organization to perform advanced analytics (including AI and Machine Learning) within a single ecosystem.
  • Embrace an open API and algorithm library model to inject created insights into workflows and maximize reuse.
  • Deliver a modular platform with the flexibility to complement existing R&D IT environments with specific components, instead of a one-size-fits-all solution.

IQVIA’s proven track record in working with clinical data comes from conducting numerous clinical trials as a Contract Research Organization (CRO) – integrating data, analytics, and technology around the specific requirements of sponsors to ask the right questions and support smarter decision making.

IQVIA has a new cloud platform that combines data from different point solutions, enabling exploration of all data. The Clinical Data Analytics Suite collects multiple sources of data for review and analysis, uses AI/ML and other techniques to create intelligent insights, and delivers those insights to appropriate workflows. These next best actions and other recommendations improve stakeholder collaboration between patients, sites, and sponsors.

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